• Ajay Chavan
• Hrushikesh Khekade
• Sakshi Patil
• Pallavi Sawant

This abstract describes a real-time object detection framework based on the You Only Look Once (YOLO) algorithm and implemented using the OpenCV library. Object detection plays a crucial role in various computer vision applications, including autonomous driving, surveillance systems, and robotics. The proposed framework aims to achieve high accuracy and real-time performance by leveraging the efficiency of YOLO and the robustness of OpenCV. The framework utilizes a deep neural network architecture to detect and classify objects in real-time video streams. By optimizing the network architecture and leveraging parallel processing capabilities, the proposed framework achieves a balance between accuracy and speed, making it suitable for real-time applications. Experimental results demonstrate the effectiveness and efficiency of the framework, showcasing its potential for a wide range of real-world applications. The Proposed System demonstrate that YOLO is effective method and comparatively fast for recognition and localization in YOLO coco dataset.

Object Detection, Real time video, YOLO coco dataset, Py Charm, OpenCV.

"YOLO based Real-Time Object Detection ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.241-245, May-2023, Available :http://www.jetir.org/papers/JETIRFX06042.pdf

"YOLO based Real-Time Object Detection ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. pp241-245, May-2023, Available at : http://www.jetir.org/papers/JETIRFX06042.pdf